Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.

ABSTRACT: Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such "novel" peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that

SUBMITTER: Weisser H 

PROVIDER: S-EPMC5703597 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature